Overview

Dataset statistics

Number of variables51
Number of observations23413
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory242.0 B

Variable types

Numeric20
Categorical31

Alerts

Monthly_Inhand_Salary is highly overall correlated with Amount_invested_monthly and 1 other fieldsHigh correlation
Num_Bank_Accounts is highly overall correlated with Num_Credit_Card and 10 other fieldsHigh correlation
Num_Credit_Card is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Num_Bank_Accounts and 7 other fieldsHigh correlation
Num_of_Loan is highly overall correlated with Num_Bank_Accounts and 9 other fieldsHigh correlation
Delay_from_due_date is highly overall correlated with Num_Bank_Accounts and 10 other fieldsHigh correlation
Num_of_Delayed_Payment is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Num_Credit_Inquiries is highly overall correlated with Num_Bank_Accounts and 6 other fieldsHigh correlation
Outstanding_Debt is highly overall correlated with Num_Bank_Accounts and 11 other fieldsHigh correlation
Credit_History_Age is highly overall correlated with Num_Bank_Accounts and 9 other fieldsHigh correlation
Amount_invested_monthly is highly overall correlated with Monthly_Inhand_SalaryHigh correlation
Monthly_Balance is highly overall correlated with Monthly_Inhand_SalaryHigh correlation
Credit_Mix is highly overall correlated with Outstanding_Debt and 3 other fieldsHigh correlation
Payment_of_Min_Amount_No is highly overall correlated with Num_Bank_Accounts and 8 other fieldsHigh correlation
Payment_of_Min_Amount_Yes is highly overall correlated with Num_Bank_Accounts and 8 other fieldsHigh correlation
Credit_Score is highly overall correlated with Num_Bank_Accounts and 8 other fieldsHigh correlation
Occupation_Accountant is highly imbalanced (65.8%)Imbalance
Occupation_Architect is highly imbalanced (66.2%)Imbalance
Occupation_Developer is highly imbalanced (66.9%)Imbalance
Occupation_Doctor is highly imbalanced (67.5%)Imbalance
Occupation_Engineer is highly imbalanced (64.7%)Imbalance
Occupation_Entrepreneur is highly imbalanced (66.1%)Imbalance
Occupation_Journalist is highly imbalanced (66.8%)Imbalance
Occupation_Lawyer is highly imbalanced (66.2%)Imbalance
Occupation_Manager is highly imbalanced (66.5%)Imbalance
Occupation_Mechanic is highly imbalanced (66.6%)Imbalance
Occupation_Media_Manager is highly imbalanced (68.1%)Imbalance
Occupation_Musician is highly imbalanced (67.4%)Imbalance
Occupation_Teacher is highly imbalanced (64.9%)Imbalance
Occupation_Writer is highly imbalanced (69.2%)Imbalance
Payment_Behaviour_Low_spent_Large_value_payments is highly imbalanced (51.9%)Imbalance
Credit_Utilization_Ratio has unique valuesUnique
Num_Bank_Accounts has 1305 (5.6%) zerosZeros
Num_of_Loan has 2301 (9.8%) zerosZeros
Delay_from_due_date has 353 (1.5%) zerosZeros
Num_of_Delayed_Payment has 522 (2.2%) zerosZeros
Num_Credit_Inquiries has 1454 (6.2%) zerosZeros
Total_EMI_per_month has 2162 (9.2%) zerosZeros
Home Equity Loan has 15672 (66.9%) zerosZeros
Mortgage Loan has 15553 (66.4%) zerosZeros
Payday Loan has 15364 (65.6%) zerosZeros
Student Loan has 15486 (66.1%) zerosZeros

Reproduction

Analysis started2023-06-05 12:57:30.462524
Analysis finished2023-06-05 12:58:20.634563
Duration50.17 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.168496
Minimum14
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size205.9 KiB
2023-06-05T20:58:20.704562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q125
median33
Q341
95-th percentile52
Maximum99
Range85
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.580624
Coefficient of variation (CV)0.3189962
Kurtosis-0.7954771
Mean33.168496
Median Absolute Deviation (MAD)8
Skewness0.17095243
Sum776574
Variance111.94961
MonotonicityNot monotonic
2023-06-05T20:58:20.797097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
33 1286
 
5.5%
28 730
 
3.1%
34 724
 
3.1%
35 719
 
3.1%
25 683
 
2.9%
37 680
 
2.9%
39 678
 
2.9%
27 676
 
2.9%
19 672
 
2.9%
24 672
 
2.9%
Other values (34) 15893
67.9%
ValueCountFrequency (%)
14 301
1.3%
15 373
1.6%
16 348
1.5%
17 366
1.6%
18 568
2.4%
19 672
2.9%
20 625
2.7%
21 629
2.7%
22 639
2.7%
23 623
2.7%
ValueCountFrequency (%)
99 1
 
< 0.1%
56 71
 
0.3%
55 289
1.2%
54 303
1.3%
53 323
1.4%
52 330
1.4%
51 258
1.1%
50 287
1.2%
49 331
1.4%
48 318
1.4%

Monthly_Inhand_Salary
Real number (ℝ)

Distinct8008
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3978.965
Minimum319.56
Maximum15204.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:20.889624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum319.56
5-th percentile843.58
Q11711.76
median3080.56
Q35138.76
95-th percentile10688.28
Maximum15204.63
Range14885.07
Interquartile range (IQR)3427

Descriptive statistics

Standard deviation3017.8009
Coefficient of variation (CV)0.75843866
Kurtosis1.7356396
Mean3978.965
Median Absolute Deviation (MAD)1537.8
Skewness1.4583806
Sum93159507
Variance9107122.1
MonotonicityNot monotonic
2023-06-05T20:58:20.982145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3080.56 3589
 
15.3%
1843.08 10
 
< 0.1%
3491.93 9
 
< 0.1%
1359.58 9
 
< 0.1%
1473.03 9
 
< 0.1%
1462.83 8
 
< 0.1%
4633.46 8
 
< 0.1%
807.6 8
 
< 0.1%
1095.81 8
 
< 0.1%
1092.94 7
 
< 0.1%
Other values (7998) 19748
84.3%
ValueCountFrequency (%)
319.56 3
< 0.1%
332.13 2
 
< 0.1%
332.43 3
< 0.1%
333.6 1
 
< 0.1%
355.21 5
< 0.1%
357.26 5
< 0.1%
358.06 2
 
< 0.1%
368.37 1
 
< 0.1%
378.99 3
< 0.1%
379.39 1
 
< 0.1%
ValueCountFrequency (%)
15204.63 3
< 0.1%
15136.7 4
< 0.1%
15115.19 2
 
< 0.1%
15101.94 1
 
< 0.1%
15091.09 2
 
< 0.1%
15090.08 1
 
< 0.1%
15066.78 3
< 0.1%
15038.32 3
< 0.1%
14978.34 5
< 0.1%
14960.25 4
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3808568
Minimum0
Maximum11
Zeros1305
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:21.067660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q38
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8098587
Coefficient of variation (CV)0.5221954
Kurtosis-0.8898335
Mean5.3808568
Median Absolute Deviation (MAD)2
Skewness-0.1964147
Sum125982
Variance7.8953058
MonotonicityNot monotonic
2023-06-05T20:58:21.134423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 3098
13.2%
7 2801
12.0%
8 2751
11.7%
4 2452
10.5%
5 2412
10.3%
3 2372
10.1%
9 1780
7.6%
10 1657
7.1%
2 1410
6.0%
1 1374
5.9%
Other values (2) 1306
5.6%
ValueCountFrequency (%)
0 1305
5.6%
1 1374
5.9%
2 1410
6.0%
3 2372
10.1%
4 2452
10.5%
5 2412
10.3%
6 3098
13.2%
7 2801
12.0%
8 2751
11.7%
9 1780
7.6%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 1657
7.1%
9 1780
7.6%
8 2751
11.7%
7 2801
12.0%
6 3098
13.2%
5 2412
10.3%
4 2452
10.5%
3 2372
10.1%
2 1410
6.0%

Num_Credit_Card
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.688421
Minimum0
Maximum11
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:21.265947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q37
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2554302
Coefficient of variation (CV)0.39649495
Kurtosis-0.507249
Mean5.688421
Median Absolute Deviation (MAD)1
Skewness0.047986512
Sum133183
Variance5.0869653
MonotonicityNot monotonic
2023-06-05T20:58:21.349470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 4238
18.1%
6 3963
16.9%
7 3630
15.5%
4 2690
11.5%
3 2508
10.7%
8 1615
 
6.9%
10 1538
 
6.6%
9 1501
 
6.4%
2 857
 
3.7%
1 855
 
3.7%
Other values (2) 18
 
0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 855
 
3.7%
2 857
 
3.7%
3 2508
10.7%
4 2690
11.5%
5 4238
18.1%
6 3963
16.9%
7 3630
15.5%
8 1615
 
6.9%
9 1501
 
6.4%
ValueCountFrequency (%)
11 13
 
0.1%
10 1538
 
6.6%
9 1501
 
6.4%
8 1615
 
6.9%
7 3630
15.5%
6 3963
16.9%
5 4238
18.1%
4 2690
11.5%
3 2508
10.7%
2 857
 
3.7%

Interest_Rate
Real number (ℝ)

Distinct494
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.264298
Minimum1
Maximum5788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:21.442984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q324
95-th percentile33
Maximum5788
Range5787
Interquartile range (IQR)17

Descriptive statistics

Standard deviation460.14734
Coefficient of variation (CV)6.3675613
Kurtosis87.387353
Mean72.264298
Median Absolute Deviation (MAD)8
Skewness9.1089655
Sum1691924
Variance211735.58
MonotonicityNot monotonic
2023-06-05T20:58:21.560052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1154
 
4.9%
5 1026
 
4.4%
11 973
 
4.2%
7 963
 
4.1%
6 919
 
3.9%
12 912
 
3.9%
9 910
 
3.9%
10 853
 
3.6%
4 844
 
3.6%
3 833
 
3.6%
Other values (484) 14026
59.9%
ValueCountFrequency (%)
1 800
3.4%
2 778
3.3%
3 833
3.6%
4 844
3.6%
5 1026
4.4%
6 919
3.9%
7 963
4.1%
8 1154
4.9%
9 910
3.9%
10 853
3.6%
ValueCountFrequency (%)
5788 1
< 0.1%
5774 1
< 0.1%
5773 1
< 0.1%
5752 1
< 0.1%
5751 1
< 0.1%
5745 1
< 0.1%
5733 1
< 0.1%
5732 2
< 0.1%
5729 1
< 0.1%
5721 1
< 0.1%

Num_of_Loan
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8287703
Minimum0
Maximum9
Zeros2301
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size205.9 KiB
2023-06-05T20:58:21.667571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.502677
Coefficient of variation (CV)0.65365034
Kurtosis-0.78268813
Mean3.8287703
Median Absolute Deviation (MAD)2
Skewness0.32006193
Sum89643
Variance6.2633923
MonotonicityNot monotonic
2023-06-05T20:58:21.740088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 3595
15.4%
4 3504
15.0%
3 3460
14.8%
0 2301
9.8%
6 2206
9.4%
1 2136
9.1%
7 2118
9.0%
5 2003
8.6%
9 1124
 
4.8%
8 966
 
4.1%
ValueCountFrequency (%)
0 2301
9.8%
1 2136
9.1%
2 3595
15.4%
3 3460
14.8%
4 3504
15.0%
5 2003
8.6%
6 2206
9.4%
7 2118
9.0%
8 966
 
4.1%
9 1124
 
4.8%
ValueCountFrequency (%)
9 1124
 
4.8%
8 966
 
4.1%
7 2118
9.0%
6 2206
9.4%
5 2003
8.6%
4 3504
15.0%
3 3460
14.8%
2 3595
15.4%
1 2136
9.1%
0 2301
9.8%

Delay_from_due_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.748664
Minimum0
Maximum67
Zeros353
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:21.823609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median19
Q331
95-th percentile56
Maximum67
Range67
Interquartile range (IQR)21

Descriptive statistics

Standard deviation16.381454
Coefficient of variation (CV)0.72010618
Kurtosis-0.34273147
Mean22.748664
Median Absolute Deviation (MAD)10
Skewness0.78434253
Sum532614.48
Variance268.35203
MonotonicityNot monotonic
2023-06-05T20:58:21.915613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 813
 
3.5%
14 758
 
3.2%
10 748
 
3.2%
7 726
 
3.1%
8 722
 
3.1%
13 705
 
3.0%
5 694
 
3.0%
11 682
 
2.9%
6 662
 
2.8%
12 660
 
2.8%
Other values (59) 16243
69.4%
ValueCountFrequency (%)
0 353
1.5%
1 383
1.6%
2 385
1.6%
3 522
2.2%
4 489
2.1%
5 694
3.0%
6 662
2.8%
7 726
3.1%
8 722
3.1%
9 653
2.8%
ValueCountFrequency (%)
67 6
 
< 0.1%
66 11
 
< 0.1%
65 14
 
0.1%
64 24
 
0.1%
63 24
 
0.1%
62 178
0.8%
61 177
0.8%
60 154
0.7%
59 171
0.7%
58 202
0.9%

Num_of_Delayed_Payment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct189
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.028446
Minimum0
Maximum4340
Zeros522
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:22.016355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median14
Q318
95-th percentile24
Maximum4340
Range4340
Interquartile range (IQR)9

Descriptive statistics

Standard deviation214.76019
Coefficient of variation (CV)7.3982668
Kurtosis241.80459
Mean29.028446
Median Absolute Deviation (MAD)5
Skewness15.128667
Sum679643
Variance46121.938
MonotonicityNot monotonic
2023-06-05T20:58:22.106906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 2555
 
10.9%
10 1166
 
5.0%
18 1153
 
4.9%
19 1152
 
4.9%
20 1139
 
4.9%
9 1112
 
4.7%
17 1104
 
4.7%
16 1070
 
4.6%
8 1067
 
4.6%
15 1046
 
4.5%
Other values (179) 10849
46.3%
ValueCountFrequency (%)
0 522
2.2%
1 531
2.3%
2 538
2.3%
3 585
2.5%
4 533
2.3%
5 621
2.7%
6 675
2.9%
7 644
2.8%
8 1067
4.6%
9 1112
4.7%
ValueCountFrequency (%)
4340 1
< 0.1%
4319 1
< 0.1%
4295 1
< 0.1%
4282 1
< 0.1%
4281 1
< 0.1%
4266 1
< 0.1%
4262 1
< 0.1%
4231 1
< 0.1%
4211 1
< 0.1%
4178 1
< 0.1%

Changed_Credit_Limit
Real number (ℝ)

Distinct3217
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6132645
Minimum-6.49
Maximum35.3
Zeros1
Zeros (%)< 0.1%
Negative414
Negative (%)1.8%
Memory size183.0 KiB
2023-06-05T20:58:22.204413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.49
5-th percentile1.07
Q14.69
median8.74
Q312.9
95-th percentile23.08
Maximum35.3
Range41.79
Interquartile range (IQR)8.21

Descriptive statistics

Standard deviation6.6376309
Coefficient of variation (CV)0.69046585
Kurtosis0.48330391
Mean9.6132645
Median Absolute Deviation (MAD)4.08
Skewness0.82104312
Sum225075.36
Variance44.058143
MonotonicityNot monotonic
2023-06-05T20:58:22.292477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.613264486 512
 
2.2%
9.25 48
 
0.2%
11.5 45
 
0.2%
7.06 36
 
0.2%
11.49 34
 
0.1%
11.95 34
 
0.1%
10.54 34
 
0.1%
1.63 34
 
0.1%
8.99 33
 
0.1%
3.93 32
 
0.1%
Other values (3207) 22571
96.4%
ValueCountFrequency (%)
-6.49 1
< 0.1%
-6.43 1
< 0.1%
-6.35 1
< 0.1%
-6.33 1
< 0.1%
-6.32 1
< 0.1%
-5.99 1
< 0.1%
-5.93 1
< 0.1%
-5.78 1
< 0.1%
-5.76 1
< 0.1%
-5.74 1
< 0.1%
ValueCountFrequency (%)
35.3 1
< 0.1%
34.91 1
< 0.1%
34.86 1
< 0.1%
34.53 1
< 0.1%
34.48 1
< 0.1%
34.3 1
< 0.1%
34.12 1
< 0.1%
34.01 1
< 0.1%
33.96 1
< 0.1%
33.61 1
< 0.1%

Num_Credit_Inquiries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct359
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.515056
Minimum0
Maximum2587
Zeros1454
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:22.384008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q310
95-th percentile14
Maximum2587
Range2587
Interquartile range (IQR)7

Descriptive statistics

Standard deviation189.41576
Coefficient of variation (CV)6.884077
Kurtosis102.61186
Mean27.515056
Median Absolute Deviation (MAD)3
Skewness9.890863
Sum644210
Variance35878.331
MonotonicityNot monotonic
2023-06-05T20:58:22.476531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2229
9.5%
4 2163
 
9.2%
3 2033
 
8.7%
2 1751
 
7.5%
8 1731
 
7.4%
7 1728
 
7.4%
1 1718
 
7.3%
11 1590
 
6.8%
9 1541
 
6.6%
0 1454
 
6.2%
Other values (349) 5475
23.4%
ValueCountFrequency (%)
0 1454
6.2%
1 1718
7.3%
2 1751
7.5%
3 2033
8.7%
4 2163
9.2%
5 930
4.0%
6 2229
9.5%
7 1728
7.4%
8 1731
7.4%
9 1541
6.6%
ValueCountFrequency (%)
2587 1
< 0.1%
2572 2
< 0.1%
2564 1
< 0.1%
2551 1
< 0.1%
2547 1
< 0.1%
2544 1
< 0.1%
2542 1
< 0.1%
2540 1
< 0.1%
2529 1
< 0.1%
2521 1
< 0.1%

Credit_Mix
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
3
12561 
1
5854 
2
4998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Length

2023-06-05T20:58:22.556678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:22.633548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring characters

ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 12561
53.6%
1 5854
25.0%
2 4998
 
21.3%

Outstanding_Debt
Real number (ℝ)

Distinct8018
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1598.1008
Minimum0.23
Maximum4998.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:22.709564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile139.77
Q1695.46
median1361.17
Q32281.65
95-th percentile4126.76
Maximum4998.07
Range4997.84
Interquartile range (IQR)1586.19

Descriptive statistics

Standard deviation1163.5777
Coefficient of variation (CV)0.72810028
Kurtosis0.37930629
Mean1598.1008
Median Absolute Deviation (MAD)763.86
Skewness0.93534478
Sum37416335
Variance1353913
MonotonicityNot monotonic
2023-06-05T20:58:22.805673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2538.81 11
 
< 0.1%
463.57 10
 
< 0.1%
1360.45 10
 
< 0.1%
2696.09 10
 
< 0.1%
3626.94 9
 
< 0.1%
2530.46 9
 
< 0.1%
66.47 9
 
< 0.1%
3572.04 9
 
< 0.1%
796.88 9
 
< 0.1%
1292.14 9
 
< 0.1%
Other values (8008) 23318
99.6%
ValueCountFrequency (%)
0.23 2
< 0.1%
0.54 2
< 0.1%
0.56 1
 
< 0.1%
0.77 2
< 0.1%
1.2 4
< 0.1%
1.3 4
< 0.1%
1.48 3
< 0.1%
2.43 4
< 0.1%
3.68 2
< 0.1%
3.74 1
 
< 0.1%
ValueCountFrequency (%)
4998.07 5
< 0.1%
4997.1 3
< 0.1%
4997.05 1
 
< 0.1%
4992.25 2
 
< 0.1%
4990.91 2
 
< 0.1%
4987.19 2
 
< 0.1%
4986.03 2
 
< 0.1%
4983.86 3
< 0.1%
4975.63 2
 
< 0.1%
4974.81 4
< 0.1%

Credit_Utilization_Ratio
Real number (ℝ)

Distinct23413
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.215666
Minimum20.172942
Maximum49.522324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:22.903895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.172942
5-th percentile24.183841
Q127.976271
median32.250408
Q336.458782
95-th percentile40.152072
Maximum49.522324
Range29.349382
Interquartile range (IQR)8.4825113

Descriptive statistics

Standard deviation5.1215648
Coefficient of variation (CV)0.15897746
Kurtosis-0.94188773
Mean32.215666
Median Absolute Deviation (MAD)4.2351899
Skewness0.032470915
Sum754265.38
Variance26.230426
MonotonicityNot monotonic
2023-06-05T20:58:22.993896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.93466475 1
 
< 0.1%
27.36771187 1
 
< 0.1%
23.55829516 1
 
< 0.1%
37.51398837 1
 
< 0.1%
32.46391157 1
 
< 0.1%
25.35531002 1
 
< 0.1%
26.56402492 1
 
< 0.1%
29.96399943 1
 
< 0.1%
23.95960253 1
 
< 0.1%
37.31756332 1
 
< 0.1%
Other values (23403) 23403
> 99.9%
ValueCountFrequency (%)
20.1729419 1
< 0.1%
20.24413035 1
< 0.1%
20.98560579 1
< 0.1%
20.98591888 1
< 0.1%
20.992914 1
< 0.1%
21.02869026 1
< 0.1%
21.22850297 1
< 0.1%
21.33717658 1
< 0.1%
21.35905054 1
< 0.1%
21.45898741 1
< 0.1%
ValueCountFrequency (%)
49.5223243 1
< 0.1%
48.48985173 1
< 0.1%
47.96956024 1
< 0.1%
47.64242451 1
< 0.1%
47.57875179 1
< 0.1%
47.48366327 1
< 0.1%
47.29400692 1
< 0.1%
47.10340881 1
< 0.1%
46.92057454 1
< 0.1%
46.64453623 1
< 0.1%

Credit_History_Age
Real number (ℝ)

Distinct405
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.49093
Minimum1
Maximum404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:23.645025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile66
Q1143
median213.49093
Q3281
95-th percentile377
Maximum404
Range403
Interquartile range (IQR)138

Descriptive statistics

Standard deviation94.831951
Coefficient of variation (CV)0.44419663
Kurtosis-0.69827891
Mean213.49093
Median Absolute Deviation (MAD)69.509074
Skewness0.053067912
Sum4998463.1
Variance8993.0989
MonotonicityNot monotonic
2023-06-05T20:58:23.745944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213.4909262 2143
 
9.2%
191 115
 
0.5%
231 113
 
0.5%
215 110
 
0.5%
213 109
 
0.5%
212 107
 
0.5%
232 105
 
0.4%
219 105
 
0.4%
233 105
 
0.4%
224 104
 
0.4%
Other values (395) 20297
86.7%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 6
 
< 0.1%
3 6
 
< 0.1%
4 11
< 0.1%
5 14
0.1%
6 11
< 0.1%
7 11
< 0.1%
8 21
0.1%
9 20
0.1%
10 20
0.1%
ValueCountFrequency (%)
404 2
 
< 0.1%
403 1
 
< 0.1%
402 10
 
< 0.1%
401 13
 
0.1%
400 21
 
0.1%
399 34
0.1%
398 41
0.2%
397 43
0.2%
396 52
0.2%
395 59
0.3%

Total_EMI_per_month
Real number (ℝ)

Distinct8198
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.8789
Minimum0
Maximum82236
Zeros2162
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:23.845958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.767386
median73.981294
Q3169.936
95-th percentile472.36108
Maximum82236
Range82236
Interquartile range (IQR)136.16862

Descriptive statistics

Standard deviation8027.0441
Coefficient of variation (CV)5.9774892
Kurtosis55.867748
Mean1342.8789
Median Absolute Deviation (MAD)52.45971
Skewness7.3245001
Sum31440824
Variance64433437
MonotonicityNot monotonic
2023-06-05T20:58:23.933481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2162
 
9.2%
218.6546697 8
 
< 0.1%
145.5524217 8
 
< 0.1%
150.2708913 8
 
< 0.1%
40.92697968 7
 
< 0.1%
77.91293447 7
 
< 0.1%
110.1315508 7
 
< 0.1%
35.38951471 7
 
< 0.1%
19.80976191 7
 
< 0.1%
146.7012526 7
 
< 0.1%
Other values (8188) 21185
90.5%
ValueCountFrequency (%)
0 2162
9.2%
4.713183572 5
 
< 0.1%
5.24927327 4
 
< 0.1%
5.262291048 3
 
< 0.1%
5.629824417 1
 
< 0.1%
5.905518076 2
 
< 0.1%
5.994895587 1
 
< 0.1%
6.047450347 3
 
< 0.1%
6.412118995 4
 
< 0.1%
6.442169892 2
 
< 0.1%
ValueCountFrequency (%)
82236 1
< 0.1%
82095 1
< 0.1%
81971 1
< 0.1%
81209 1
< 0.1%
80981 1
< 0.1%
80768 1
< 0.1%
80679 1
< 0.1%
80602 1
< 0.1%
80501 1
< 0.1%
80497 1
< 0.1%

Amount_invested_monthly
Real number (ℝ)

Distinct21223
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623.40594
Minimum0
Maximum10000
Zeros56
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:24.026111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.160878
Q176.235889
median134.35556
Q3252.70465
95-th percentile1145.1946
Maximum10000
Range10000
Interquartile range (IQR)176.46876

Descriptive statistics

Standard deviation2018.9814
Coefficient of variation (CV)3.2386304
Kurtosis17.435479
Mean623.40594
Median Absolute Deviation (MAD)70.798083
Skewness4.3860455
Sum14595803
Variance4076285.9
MonotonicityNot monotonic
2023-06-05T20:58:24.121109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134.35556 1109
 
4.7%
10000 1028
 
4.4%
0 56
 
0.2%
208.8959183 1
 
< 0.1%
232.1775582 1
 
< 0.1%
48.00806666 1
 
< 0.1%
90.84269353 1
 
< 0.1%
136.0445319 1
 
< 0.1%
204.6386045 1
 
< 0.1%
145.5815241 1
 
< 0.1%
Other values (21213) 21213
90.6%
ValueCountFrequency (%)
0 56
0.2%
10.11661404 1
 
< 0.1%
10.1225566 1
 
< 0.1%
10.14128456 1
 
< 0.1%
10.14343562 1
 
< 0.1%
10.20080048 1
 
< 0.1%
10.2494613 1
 
< 0.1%
10.28340438 1
 
< 0.1%
10.33620331 1
 
< 0.1%
10.35788829 1
 
< 0.1%
ValueCountFrequency (%)
10000 1028
4.4%
1961.21885 1
 
< 0.1%
1941.237454 1
 
< 0.1%
1885.645318 1
 
< 0.1%
1860.919693 1
 
< 0.1%
1804.332527 1
 
< 0.1%
1804.233521 1
 
< 0.1%
1785.786788 1
 
< 0.1%
1729.241436 1
 
< 0.1%
1684.861491 1
 
< 0.1%

Monthly_Balance
Real number (ℝ)

Distinct23125
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.05717
Minimum0.095482496
Maximum1602.0405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:24.220046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.095482496
5-th percentile171.36689
Q1266.9128
median330.56261
Q3452.53029
95-th percentile867.08216
Maximum1602.0405
Range1601.945
Interquartile range (IQR)185.61749

Descriptive statistics

Standard deviation214.43463
Coefficient of variation (CV)0.54279392
Kurtosis3.5236459
Mean395.05717
Median Absolute Deviation (MAD)79.41909
Skewness1.7385404
Sum9249473.4
Variance45982.209
MonotonicityNot monotonic
2023-06-05T20:58:24.313342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
395.0571653 289
 
1.2%
246.4845984 1
 
< 0.1%
814.432899 1
 
< 0.1%
833.4292499 1
 
< 0.1%
288.3921991 1
 
< 0.1%
299.542164 1
 
< 0.1%
296.5068107 1
 
< 0.1%
373.2708014 1
 
< 0.1%
240.0178673 1
 
< 0.1%
239.245076 1
 
< 0.1%
Other values (23115) 23115
98.7%
ValueCountFrequency (%)
0.09548249602 1
< 0.1%
0.3661470795 1
< 0.1%
0.4191236108 1
< 0.1%
0.4534564914 1
< 0.1%
0.5035823529 1
< 0.1%
0.599640126 1
< 0.1%
0.688298779 1
< 0.1%
0.7102397102 1
< 0.1%
0.9081458437 1
< 0.1%
1.526310326 1
< 0.1%
ValueCountFrequency (%)
1602.040519 1
< 0.1%
1566.613165 1
< 0.1%
1558.421841 1
< 0.1%
1555.201051 1
< 0.1%
1528.744936 1
< 0.1%
1507.553363 1
< 0.1%
1478.421322 1
< 0.1%
1474.356118 1
< 0.1%
1468.313963 1
< 0.1%
1463.792328 1
< 0.1%

Auto Loan
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
15722 
1
6041 
2
 
1395
3
 
232
4
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 15722
67.2%
1 6041
 
25.8%
2 1395
 
6.0%
3 232
 
1.0%
4 23
 
0.1%

Length

2023-06-05T20:58:24.400853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:24.472374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15722
67.2%
1 6041
 
25.8%
2 1395
 
6.0%
3 232
 
1.0%
4 23
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15722
67.2%
1 6041
 
25.8%
2 1395
 
6.0%
3 232
 
1.0%
4 23
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15722
67.2%
1 6041
 
25.8%
2 1395
 
6.0%
3 232
 
1.0%
4 23
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15722
67.2%
1 6041
 
25.8%
2 1395
 
6.0%
3 232
 
1.0%
4 23
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15722
67.2%
1 6041
 
25.8%
2 1395
 
6.0%
3 232
 
1.0%
4 23
 
0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
15373 
1
6120 
2
1617 
3
 
253
4
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 15373
65.7%
1 6120
 
26.1%
2 1617
 
6.9%
3 253
 
1.1%
4 50
 
0.2%

Length

2023-06-05T20:58:24.536834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:24.608836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15373
65.7%
1 6120
 
26.1%
2 1617
 
6.9%
3 253
 
1.1%
4 50
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15373
65.7%
1 6120
 
26.1%
2 1617
 
6.9%
3 253
 
1.1%
4 50
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15373
65.7%
1 6120
 
26.1%
2 1617
 
6.9%
3 253
 
1.1%
4 50
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15373
65.7%
1 6120
 
26.1%
2 1617
 
6.9%
3 253
 
1.1%
4 50
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15373
65.7%
1 6120
 
26.1%
2 1617
 
6.9%
3 253
 
1.1%
4 50
 
0.2%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
15684 
1
5871 
2
1615 
3
 
215
4
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15684
67.0%
1 5871
 
25.1%
2 1615
 
6.9%
3 215
 
0.9%
4 28
 
0.1%

Length

2023-06-05T20:58:24.674354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:24.746868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15684
67.0%
1 5871
 
25.1%
2 1615
 
6.9%
3 215
 
0.9%
4 28
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15684
67.0%
1 5871
 
25.1%
2 1615
 
6.9%
3 215
 
0.9%
4 28
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15684
67.0%
1 5871
 
25.1%
2 1615
 
6.9%
3 215
 
0.9%
4 28
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15684
67.0%
1 5871
 
25.1%
2 1615
 
6.9%
3 215
 
0.9%
4 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15684
67.0%
1 5871
 
25.1%
2 1615
 
6.9%
3 215
 
0.9%
4 28
 
0.1%

Home Equity Loan
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41583736
Minimum0
Maximum5
Zeros15672
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:24.806873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.66532222
Coefficient of variation (CV)1.5999578
Kurtosis3.1076433
Mean0.41583736
Median Absolute Deviation (MAD)0
Skewness1.6807758
Sum9736
Variance0.44265366
MonotonicityNot monotonic
2023-06-05T20:58:24.868394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15672
66.9%
1 6032
 
25.8%
2 1474
 
6.3%
3 190
 
0.8%
4 39
 
0.2%
5 6
 
< 0.1%
ValueCountFrequency (%)
0 15672
66.9%
1 6032
 
25.8%
2 1474
 
6.3%
3 190
 
0.8%
4 39
 
0.2%
5 6
 
< 0.1%
ValueCountFrequency (%)
5 6
 
< 0.1%
4 39
 
0.2%
3 190
 
0.8%
2 1474
 
6.3%
1 6032
 
25.8%
0 15672
66.9%

Mortgage Loan
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41993764
Minimum0
Maximum5
Zeros15553
Zeros (%)66.4%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:24.929911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.66106393
Coefficient of variation (CV)1.5741955
Kurtosis2.678907
Mean0.41993764
Median Absolute Deviation (MAD)0
Skewness1.6018003
Sum9832
Variance0.43700551
MonotonicityNot monotonic
2023-06-05T20:58:24.993751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15553
66.4%
1 6141
 
26.2%
2 1496
 
6.4%
3 202
 
0.9%
4 12
 
0.1%
5 9
 
< 0.1%
ValueCountFrequency (%)
0 15553
66.4%
1 6141
 
26.2%
2 1496
 
6.4%
3 202
 
0.9%
4 12
 
0.1%
5 9
 
< 0.1%
ValueCountFrequency (%)
5 9
 
< 0.1%
4 12
 
0.1%
3 202
 
0.9%
2 1496
 
6.4%
1 6141
 
26.2%
0 15553
66.4%

Not Specified
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
15273 
1
6393 
2
 
1491
3
 
227
4
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 15273
65.2%
1 6393
27.3%
2 1491
 
6.4%
3 227
 
1.0%
4 29
 
0.1%

Length

2023-06-05T20:58:25.063265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:25.138780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15273
65.2%
1 6393
27.3%
2 1491
 
6.4%
3 227
 
1.0%
4 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15273
65.2%
1 6393
27.3%
2 1491
 
6.4%
3 227
 
1.0%
4 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15273
65.2%
1 6393
27.3%
2 1491
 
6.4%
3 227
 
1.0%
4 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15273
65.2%
1 6393
27.3%
2 1491
 
6.4%
3 227
 
1.0%
4 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15273
65.2%
1 6393
27.3%
2 1491
 
6.4%
3 227
 
1.0%
4 29
 
0.1%

Payday Loan
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44419767
Minimum0
Maximum5
Zeros15364
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:25.199787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.69579122
Coefficient of variation (CV)1.5664
Kurtosis2.6910499
Mean0.44419767
Median Absolute Deviation (MAD)0
Skewness1.625975
Sum10400
Variance0.48412542
MonotonicityNot monotonic
2023-06-05T20:58:25.262831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15364
65.6%
1 6069
 
25.9%
2 1659
 
7.1%
3 276
 
1.2%
4 40
 
0.2%
5 5
 
< 0.1%
ValueCountFrequency (%)
0 15364
65.6%
1 6069
 
25.9%
2 1659
 
7.1%
3 276
 
1.2%
4 40
 
0.2%
5 5
 
< 0.1%
ValueCountFrequency (%)
5 5
 
< 0.1%
4 40
 
0.2%
3 276
 
1.2%
2 1659
 
7.1%
1 6069
 
25.9%
0 15364
65.6%

Personal Loan
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
15824 
1
5839 
2
 
1493
3
 
245
4
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15824
67.6%
1 5839
 
24.9%
2 1493
 
6.4%
3 245
 
1.0%
4 12
 
0.1%

Length

2023-06-05T20:58:25.331344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:25.408315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15824
67.6%
1 5839
 
24.9%
2 1493
 
6.4%
3 245
 
1.0%
4 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15824
67.6%
1 5839
 
24.9%
2 1493
 
6.4%
3 245
 
1.0%
4 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15824
67.6%
1 5839
 
24.9%
2 1493
 
6.4%
3 245
 
1.0%
4 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15824
67.6%
1 5839
 
24.9%
2 1493
 
6.4%
3 245
 
1.0%
4 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15824
67.6%
1 5839
 
24.9%
2 1493
 
6.4%
3 245
 
1.0%
4 12
 
0.1%

Student Loan
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43159783
Minimum0
Maximum5
Zeros15486
Zeros (%)66.1%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-06-05T20:58:25.469842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6804394
Coefficient of variation (CV)1.5765589
Kurtosis2.6814734
Mean0.43159783
Median Absolute Deviation (MAD)0
Skewness1.6264789
Sum10105
Variance0.46299778
MonotonicityNot monotonic
2023-06-05T20:58:25.533363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15486
66.1%
1 6071
 
25.9%
2 1580
 
6.7%
3 232
 
1.0%
4 42
 
0.2%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 15486
66.1%
1 6071
 
25.9%
2 1580
 
6.7%
3 232
 
1.0%
4 42
 
0.2%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 42
 
0.2%
3 232
 
1.0%
2 1580
 
6.7%
1 6071
 
25.9%
0 15486
66.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21920 
1
 
1493

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Length

2023-06-05T20:58:25.601360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:25.667883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21920
93.6%
1 1493
 
6.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21945 
1
 
1468

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Length

2023-06-05T20:58:25.724398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:25.790404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21945
93.7%
1 1468
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21989 
1
 
1424

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Length

2023-06-05T20:58:25.852976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:25.921496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21989
93.9%
1 1424
 
6.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22024 
1
 
1389

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Length

2023-06-05T20:58:25.976502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:26.043031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22024
94.1%
1 1389
 
5.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21853 
1
 
1560

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Length

2023-06-05T20:58:26.099032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:26.171553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21853
93.3%
1 1560
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21937 
1
 
1476

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Length

2023-06-05T20:58:26.237697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:26.303696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21937
93.7%
1 1476
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21982 
1
 
1431

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Length

2023-06-05T20:58:26.360661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:26.426056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21982
93.9%
1 1431
 
6.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21943 
1
 
1470

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Length

2023-06-05T20:58:26.504534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:26.581635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21943
93.7%
1 1470
 
6.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21965 
1
 
1448

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Length

2023-06-05T20:58:26.643765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:26.739916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21965
93.8%
1 1448
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21967 
1
 
1446

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Length

2023-06-05T20:58:26.800920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:26.873436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21967
93.8%
1 1446
 
6.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22059 
1
 
1354

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Length

2023-06-05T20:58:26.937574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.005573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22059
94.2%
1 1354
 
5.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22017 
1
 
1396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Length

2023-06-05T20:58:27.069096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.147231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22017
94.0%
1 1396
 
6.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20194 
1
3219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Length

2023-06-05T20:58:27.213230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.283761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20194
86.3%
1 3219
 
13.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
21867 
1
 
1546

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Length

2023-06-05T20:58:27.343269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.415658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21867
93.4%
1 1546
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
22120 
1
 
1293

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Length

2023-06-05T20:58:27.472180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.537697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22120
94.5%
1 1293
 
5.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20507 
1
2906 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Length

2023-06-05T20:58:27.593904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.660434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20507
87.6%
1 2906
 
12.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
14273 
1
9140 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Length

2023-06-05T20:58:27.719856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.785364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring characters

ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14273
61.0%
1 9140
39.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
12046 
1
11367 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Length

2023-06-05T20:58:27.844887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:27.919897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring characters

ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12046
51.5%
1 11367
48.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20303 
1
3110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Length

2023-06-05T20:58:27.981930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:28.052450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20303
86.7%
1 3110
 
13.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
19528 
1
3885 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Length

2023-06-05T20:58:28.111598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:28.181688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19528
83.4%
1 3885
 
16.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20782 
1
2631 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Length

2023-06-05T20:58:28.260208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:28.398729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20782
88.8%
1 2631
 
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20985 
1
2428 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Length

2023-06-05T20:58:28.488248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:28.574779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20985
89.6%
1 2428
 
10.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
20033 
1
3380 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Length

2023-06-05T20:58:28.645094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:28.722099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20033
85.6%
1 3380
 
14.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
15434 
1
7979 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Length

2023-06-05T20:58:28.782360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:28.853386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring characters

ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15434
65.9%
1 7979
34.1%

Credit_Score
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
0
14499 
1
8914 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23413
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Length

2023-06-05T20:58:28.918392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-05T20:58:28.992917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring characters

ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23413
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14499
61.9%
1 8914
38.1%

Interactions

2023-06-05T20:58:17.301427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.272779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:42.992496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.297935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.138304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.217082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.158079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:52.857190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:54.877849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:56.604745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.558279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.296537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:02.036345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:03.848015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:06.043226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:07.843925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.595883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.496572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:13.723968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.539526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.383951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.371792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:43.078369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.381452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.225815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.300868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.239591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:53.161865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:54.958369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:56.686097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.636563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.377043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:02.123862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:03.937242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:06.131744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:07.922446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.682408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.576104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:13.805981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.656822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.490669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.469953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:43.185437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.475707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.354726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.393898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.329109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:53.265324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:55.045888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:56.775287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.725083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.468105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:02.233379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:04.037066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:06.235259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:08.015227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.777932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.664624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:13.896499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.758338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.578194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.558479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:43.285957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.558234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.496244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.481408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.409624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:53.351839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:55.131039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:56.859809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.808593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.554434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:02.320891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:04.133111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:06.321779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:08.100231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.864458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.754138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:13.976027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.840860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.659523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.640997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:43.388485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.642756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.601766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.569386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.490637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:53.438353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:55.223252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:56.944385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.889601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.643957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:02.407417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:04.229631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:06.402789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:08.195740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.964551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.848659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:14.059551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.918370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.745042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.724518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:43.483591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.732289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.699859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.663865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.574715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:53.530873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:55.308776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:57.031905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.978631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.734812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:02.497937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:04.323154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:06.494865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:08.291800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:10.061069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.937994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:14.142075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:16.002386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.826559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.799632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:43.569655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.815938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.781893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.751381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.649229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:53.614391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:55.384773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:57.111427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:59.058161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.818335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:02.587024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:04.411165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:06.580397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:08.372400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:10.155177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:12.018520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:14.227710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:16.087407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.913566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:41.885141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:43.662683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.913469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.881367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.852436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.753757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:53.705399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:55.473294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:57.200436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-06-05T20:58:01.782011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:03.585188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:05.777230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:07.563375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.340938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.219542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:13.008025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.264632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.041850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:18.854139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:42.835421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.095896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:46.909377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.030045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:50.982051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:52.683685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:54.702811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:56.436307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.119034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.119187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:01.863458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:03.669399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:05.863758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:07.657418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.429463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.309547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:13.550759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.358821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.125373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:18.939661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:42.915487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:45.190414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:47.010894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:49.120560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:51.068566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:52.773077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:54.792334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:56.522126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:57:58.201037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:00.211530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:01.947985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:03.757486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:05.953196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:07.749949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:09.512965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:11.404062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:13.633287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:15.448339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-05T20:58:17.213912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-05T20:58:29.103432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AgeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioCredit_History_AgeTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceHome Equity LoanMortgage LoanPayday LoanStudent LoanCredit_MixAuto LoanCredit-Builder LoanDebt Consolidation LoanNot SpecifiedPersonal LoanOccupation_AccountantOccupation_ArchitectOccupation_DeveloperOccupation_DoctorOccupation_EngineerOccupation_EntrepreneurOccupation_JournalistOccupation_LawyerOccupation_ManagerOccupation_MechanicOccupation_Media_ManagerOccupation_MusicianOccupation_ScientistOccupation_TeacherOccupation_WriterPayment_of_Min_Amount_NMPayment_of_Min_Amount_NoPayment_of_Min_Amount_YesPayment_Behaviour_High_spent_Large_value_paymentsPayment_Behaviour_High_spent_Medium_value_paymentsPayment_Behaviour_High_spent_Small_value_paymentsPayment_Behaviour_Low_spent_Large_value_paymentsPayment_Behaviour_Low_spent_Medium_value_paymentsPayment_Behaviour_Low_spent_Small_value_paymentsCredit_Score
Age1.0000.079-0.210-0.163-0.230-0.207-0.197-0.197-0.131-0.228-0.2390.0180.217-0.0750.0560.130-0.082-0.086-0.088-0.0620.1830.0520.0510.0580.0570.0620.0000.0230.0070.0240.0220.0260.0330.0180.0270.0070.0000.0260.0000.0310.0220.0000.3050.2960.0530.0000.0000.0000.0120.0490.253
Monthly_Inhand_Salary0.0791.000-0.269-0.221-0.284-0.235-0.242-0.251-0.134-0.268-0.2890.1230.2600.4430.5220.534-0.087-0.089-0.092-0.1020.2260.0580.0740.0760.0600.0680.0260.0170.0240.0240.0290.0200.0220.0280.0390.0130.0240.0300.0150.0210.0280.0000.3560.3490.2240.1790.0620.0380.1110.2670.312
Num_Bank_Accounts-0.210-0.2691.0000.5270.6310.5480.6490.6400.2680.6030.626-0.087-0.5690.132-0.194-0.3640.2330.2460.2320.2060.4900.1170.1330.1280.1150.1260.0160.0290.0340.0240.0440.0320.0200.0330.0230.0180.0280.0360.0190.0380.0300.0000.6680.6530.0950.0390.0130.0080.0000.1090.563
Num_Credit_Card-0.163-0.2210.5271.0000.5150.4460.5320.4810.2120.4990.533-0.064-0.4670.103-0.153-0.2970.1820.1850.1940.1780.3740.1060.1060.1020.0930.1040.0200.0000.0470.0330.0000.0420.0350.0220.0340.0310.0200.0250.0170.0250.0220.0110.5220.5160.0750.0220.0000.0000.0220.0840.550
Interest_Rate-0.230-0.2840.6310.5151.0000.5760.6010.5810.3190.6570.681-0.082-0.6170.148-0.199-0.3810.2400.2440.2470.2260.0000.0000.0050.0000.0090.0260.0000.0000.0090.0070.0000.0000.0120.0000.0090.0040.0090.0120.0110.0000.0150.0000.0150.0110.0000.0180.0000.0090.0180.0090.021
Num_of_Loan-0.207-0.2350.5480.4460.5761.0000.5240.5270.2650.5640.646-0.104-0.5860.479-0.173-0.4970.4160.4150.4140.4020.4670.2150.2260.2200.2190.2210.0190.0260.0420.0400.0250.0290.0230.0500.0370.0220.0510.0340.0000.0330.0000.0120.6320.6180.0940.0310.0000.0000.0190.1030.545
Delay_from_due_date-0.197-0.2420.6490.5320.6010.5241.0000.5960.2390.5780.613-0.072-0.5500.145-0.175-0.3420.2160.2240.2180.2000.4560.1170.1160.1220.1120.1210.0180.0420.0150.0280.0190.0100.0370.0130.0230.0170.0090.0210.0240.0350.0310.0000.5890.5780.0860.0390.0000.0000.0070.0960.584
Num_of_Delayed_Payment-0.197-0.2510.6400.4810.5810.5270.5961.0000.2220.5480.588-0.087-0.5360.141-0.177-0.3510.2250.2310.2140.2040.0000.0000.0000.0000.0000.0310.0000.0000.0040.0000.0000.0000.0000.0140.0190.0110.0110.0000.0000.0080.0130.0000.0000.0000.0230.0040.0220.0110.0000.0000.001
Changed_Credit_Limit-0.131-0.1340.2680.2120.3190.2650.2390.2221.0000.3130.352-0.041-0.3400.074-0.094-0.1740.1000.1070.1060.1060.3570.0790.0900.0890.0920.0800.0310.0150.0220.0080.0130.0270.0270.0070.0290.0370.0230.0000.0000.0020.0260.0000.4150.4050.0540.0070.0160.0000.0000.0480.288
Num_Credit_Inquiries-0.228-0.2680.6030.4990.6570.5640.5780.5480.3131.0000.658-0.084-0.6130.153-0.182-0.3730.2310.2360.2380.2190.0100.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0050.0000.0000.0070.0280.0050.0110.0000.0050.0060.0000.0150.0100.0000.0000.0110.0000.0000.000
Outstanding_Debt-0.239-0.2890.6260.5330.6810.6460.6130.5880.3520.6581.000-0.088-0.6750.165-0.200-0.4110.2670.2730.2660.2600.5130.1520.1530.1500.1470.1440.0100.0270.0360.0190.0410.0300.0240.0230.0500.0390.0330.0350.0000.0340.0200.0000.7030.6900.1120.0420.0000.0000.0000.1210.648
Credit_Utilization_Ratio0.0180.123-0.087-0.064-0.082-0.104-0.072-0.087-0.041-0.084-0.0881.0000.0680.0120.0310.190-0.044-0.043-0.036-0.0500.0820.0320.0290.0340.0310.0290.0000.0000.0140.0000.0080.0130.0050.0030.0090.0000.0150.0000.0000.0000.0110.0040.1250.1200.1520.0560.0160.0290.0420.0920.097
Credit_History_Age0.2170.260-0.569-0.467-0.617-0.586-0.550-0.536-0.340-0.613-0.6750.0681.000-0.1560.1830.373-0.228-0.244-0.245-0.2420.4270.1230.1320.1290.1230.1260.0150.0230.0270.0220.0000.0430.0250.0250.0260.0340.0150.0120.0280.0260.0290.0140.6500.6390.0950.0290.0190.0070.0000.1050.562
Total_EMI_per_month-0.0750.4430.1320.1030.1480.4790.1450.1410.0740.1530.1650.012-0.1561.0000.2690.0300.2150.2130.1860.1890.0000.0000.0000.0000.0230.0140.0000.0000.0140.0120.0000.0000.0000.0000.0000.0000.0050.0140.0000.0100.0000.0070.0000.0000.0090.0180.0000.0080.0030.0000.013
Amount_invested_monthly0.0560.522-0.194-0.153-0.199-0.173-0.175-0.177-0.094-0.182-0.2000.0310.1830.2691.000-0.005-0.059-0.059-0.075-0.0710.0520.0140.0190.0160.0280.0120.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0100.0050.0110.0000.0000.0080.0860.0840.0400.0460.0350.0000.0600.0440.105
Monthly_Balance0.1300.534-0.364-0.297-0.381-0.497-0.342-0.351-0.174-0.373-0.4110.1900.3730.030-0.0051.000-0.210-0.200-0.197-0.2020.2590.0960.1030.1070.0950.1040.0070.0030.0110.0070.0160.0000.0230.0190.0190.0120.0040.0250.0000.0000.0110.0000.3940.3850.4090.2670.0830.0890.1050.3490.325
Home Equity Loan-0.082-0.0870.2330.1820.2400.4160.2160.2250.1000.2310.267-0.044-0.2280.215-0.059-0.2101.0000.0740.0880.0610.1770.0390.0490.0460.0530.0510.0140.0240.0120.0000.0220.0180.0640.0160.0150.0260.0130.0260.0210.0310.0300.0000.2370.2320.0280.0190.0070.0030.0130.0380.207
Mortgage Loan-0.086-0.0890.2460.1850.2440.4150.2240.2310.1070.2360.273-0.043-0.2440.213-0.059-0.2000.0741.0000.0630.0690.1840.0460.0440.0510.0520.0440.0380.0210.0430.0150.0240.0240.0180.0330.0110.0310.0320.0220.0040.0500.0070.0000.2440.2350.0380.0150.0000.0000.0110.0500.207
Payday Loan-0.088-0.0920.2320.1940.2470.4140.2180.2140.1060.2380.266-0.036-0.2450.186-0.075-0.1970.0880.0631.0000.0750.1770.0500.0450.0370.0360.0340.0180.0260.0290.0160.0120.0420.0160.0300.0150.0200.0300.0000.0280.0140.0110.0000.2540.2450.0340.0120.0000.0000.0100.0380.210
Student Loan-0.062-0.1020.2060.1780.2260.4020.2000.2040.1060.2190.260-0.050-0.2420.189-0.071-0.2020.0610.0690.0751.0000.1580.0380.0400.0420.0510.0410.0150.0200.0090.0260.0170.0180.0000.0050.0210.0190.0110.0210.0270.0270.0040.0040.2330.2200.0320.0000.0000.0000.0210.0360.199
Credit_Mix0.1830.2260.4900.3740.0000.4670.4560.0000.3570.0100.5130.0820.4270.0000.0520.2590.1770.1840.1770.1581.0000.1670.1780.1770.1560.1720.0000.0000.0000.0110.0060.0000.0100.0000.0220.0200.0260.0130.0000.0000.0260.0000.6630.6510.0900.0310.0090.0000.0040.0950.533
Auto Loan0.0520.0580.1170.1060.0000.2150.1170.0000.0790.0000.1520.0320.1230.0000.0140.0960.0390.0460.0500.0380.1671.0000.0470.0430.0330.0440.0160.0140.0120.0210.0190.0140.0000.0150.0120.0190.0200.0250.0050.0260.0180.0050.2470.2340.0310.0080.0000.0000.0210.0310.212
Credit-Builder Loan0.0510.0740.1330.1060.0050.2260.1160.0000.0900.0000.1530.0290.1320.0000.0190.1030.0490.0440.0450.0400.1780.0471.0000.0400.0410.0470.0280.0230.0240.0010.0120.0120.0000.0160.0100.0120.0130.0070.0000.0100.0110.0100.2440.2420.0240.0110.0110.0000.0000.0450.198
Debt Consolidation Loan0.0580.0760.1280.1020.0000.2200.1220.0000.0890.0000.1500.0340.1290.0000.0160.1070.0460.0510.0370.0420.1770.0430.0401.0000.0460.0500.0260.0210.0090.0270.0310.0520.0270.0160.0220.0100.0300.0220.0030.0270.0070.0000.2430.2360.0470.0120.0000.0070.0000.0480.217
Not Specified0.0570.0600.1150.0930.0090.2190.1120.0000.0920.0000.1470.0310.1230.0230.0280.0950.0530.0520.0360.0510.1560.0330.0410.0461.0000.0370.0000.0000.0280.0230.0070.0150.0110.0160.0110.0000.0110.0320.0110.0300.0160.0000.2260.2220.0310.0000.0000.0060.0160.0370.197
Personal Loan0.0620.0680.1260.1040.0260.2210.1210.0310.0800.0000.1440.0290.1260.0140.0120.1040.0510.0440.0340.0410.1720.0440.0470.0500.0371.0000.0240.0190.0160.0160.0120.0080.0060.0200.0430.0040.0170.0360.0110.0000.0150.0020.2380.2380.0320.0190.0000.0000.0020.0450.213
Occupation_Accountant0.0000.0260.0160.0200.0000.0190.0180.0000.0310.0000.0100.0000.0150.0000.0000.0070.0140.0380.0180.0150.0000.0160.0280.0260.0000.0241.0000.0670.0660.0650.0690.0670.0660.0670.0660.0660.0640.0650.1040.0690.0620.0130.0000.0000.0000.0000.0040.0000.0000.0000.004
Occupation_Architect0.0230.0170.0290.0000.0000.0260.0420.0000.0150.0000.0270.0000.0230.0000.0000.0030.0240.0210.0260.0200.0000.0140.0230.0210.0000.0190.0671.0000.0650.0640.0680.0660.0650.0660.0660.0660.0630.0640.1030.0680.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Occupation_Developer0.0070.0240.0340.0470.0090.0420.0150.0040.0220.0000.0360.0140.0270.0140.0000.0110.0120.0430.0290.0090.0000.0120.0240.0090.0280.0160.0660.0651.0000.0630.0670.0650.0640.0650.0650.0650.0620.0630.1010.0670.0610.0000.0100.0030.0000.0040.0040.0000.0030.0000.004
Occupation_Doctor0.0240.0240.0240.0330.0070.0400.0280.0000.0080.0110.0190.0000.0220.0120.0000.0070.0000.0150.0160.0260.0110.0210.0010.0270.0230.0160.0650.0640.0631.0000.0660.0640.0630.0640.0640.0640.0610.0630.1000.0660.0600.0000.0080.0100.0000.0080.0000.0050.0120.0000.000
Occupation_Engineer0.0220.0290.0440.0000.0000.0250.0190.0000.0130.0000.0410.0080.0000.0000.0050.0160.0220.0240.0120.0170.0060.0190.0120.0310.0070.0120.0690.0680.0670.0661.0000.0690.0670.0680.0680.0680.0660.0670.1060.0700.0640.0000.0000.0000.0070.0000.0040.0040.0000.0000.000
Occupation_Entrepreneur0.0260.0200.0320.0420.0000.0290.0100.0000.0270.0000.0300.0130.0430.0000.0000.0000.0180.0240.0420.0180.0000.0140.0120.0520.0150.0080.0670.0660.0650.0640.0691.0000.0650.0660.0660.0660.0640.0650.1030.0680.0620.0010.0000.0000.0040.0000.0000.0000.0000.0000.005
Occupation_Journalist0.0330.0220.0200.0350.0120.0230.0370.0000.0270.0050.0240.0050.0250.0000.0000.0230.0640.0180.0160.0000.0100.0000.0000.0270.0110.0060.0660.0650.0640.0630.0670.0651.0000.0650.0650.0650.0620.0640.1010.0670.0610.0030.0000.0000.0000.0000.0070.0000.0000.0000.008
Occupation_Lawyer0.0180.0280.0330.0220.0000.0500.0130.0140.0070.0000.0230.0030.0250.0000.0000.0190.0160.0330.0300.0050.0000.0150.0160.0160.0160.0200.0670.0660.0650.0640.0680.0660.0651.0000.0660.0660.0630.0640.1030.0680.0620.0060.0000.0000.0060.0000.0000.0000.0000.0000.000
Occupation_Manager0.0270.0390.0230.0340.0090.0370.0230.0190.0290.0000.0500.0090.0260.0000.0000.0190.0150.0110.0150.0210.0220.0120.0100.0220.0110.0430.0660.0660.0650.0640.0680.0660.0650.0661.0000.0650.0630.0640.1020.0680.0610.0050.0050.0120.0000.0100.0050.0000.0030.0000.000
Occupation_Mechanic0.0070.0130.0180.0310.0040.0220.0170.0110.0370.0070.0390.0000.0340.0000.0000.0120.0260.0310.0200.0190.0200.0190.0120.0100.0000.0040.0660.0660.0650.0640.0680.0660.0650.0660.0651.0000.0630.0640.1020.0680.0610.0000.0000.0000.0010.0000.0000.0070.0000.0000.011
Occupation_Media_Manager0.0000.0240.0280.0200.0090.0510.0090.0110.0230.0280.0330.0150.0150.0050.0100.0040.0130.0320.0300.0110.0260.0200.0130.0300.0110.0170.0640.0630.0620.0610.0660.0640.0620.0630.0630.0631.0000.0620.0980.0650.0590.0110.0060.0160.0000.0000.0090.0000.0020.0000.019
Occupation_Musician0.0260.0300.0360.0250.0120.0340.0210.0000.0000.0050.0350.0000.0120.0140.0050.0250.0260.0220.0000.0210.0130.0250.0070.0220.0320.0360.0650.0640.0630.0630.0670.0650.0640.0640.0640.0640.0621.0000.1000.0660.0600.0000.0100.0070.0080.0000.0070.0000.0000.0000.016
Occupation_Scientist0.0000.0150.0190.0170.0110.0000.0240.0000.0000.0110.0000.0000.0280.0000.0110.0000.0210.0040.0280.0270.0000.0050.0000.0030.0110.0110.1040.1030.1010.1000.1060.1030.1010.1030.1020.1020.0980.1001.0000.1060.0960.0000.0000.0000.0000.0000.0080.0000.0040.0000.000
Occupation_Teacher0.0310.0210.0380.0250.0000.0330.0350.0080.0020.0000.0340.0000.0260.0100.0000.0000.0310.0500.0140.0270.0000.0260.0100.0270.0300.0000.0690.0680.0670.0660.0700.0680.0670.0680.0680.0680.0650.0660.1061.0000.0640.0000.0070.0080.0000.0000.0060.0000.0000.0000.000
Occupation_Writer0.0220.0280.0300.0220.0150.0000.0310.0130.0260.0050.0200.0110.0290.0000.0000.0110.0300.0070.0110.0040.0260.0180.0110.0070.0160.0150.0620.0620.0610.0600.0640.0620.0610.0620.0610.0610.0590.0600.0960.0641.0000.0000.0100.0090.0000.0000.0070.0000.0040.0000.024
Payment_of_Min_Amount_NM0.0000.0000.0000.0110.0000.0120.0000.0000.0000.0060.0000.0040.0140.0070.0080.0000.0000.0000.0000.0040.0000.0050.0100.0000.0000.0020.0130.0000.0000.0000.0000.0010.0030.0060.0050.0000.0110.0000.0000.0000.0001.0000.3010.3650.0000.0000.0060.0000.0040.0000.003
Payment_of_Min_Amount_No0.3050.3560.6680.5220.0150.6320.5890.0000.4150.0000.7030.1250.6500.0000.0860.3940.2370.2440.2540.2330.6630.2470.2440.2430.2260.2380.0000.0000.0100.0080.0000.0000.0000.0000.0050.0000.0060.0100.0000.0070.0100.3011.0000.7770.1020.0310.0130.0000.0000.1110.594
Payment_of_Min_Amount_Yes0.2960.3490.6530.5160.0110.6180.5780.0000.4050.0150.6900.1200.6390.0000.0840.3850.2320.2350.2450.2200.6510.2340.2420.2360.2220.2380.0000.0000.0030.0100.0000.0000.0000.0000.0120.0000.0160.0070.0000.0080.0090.3650.7771.0000.1010.0320.0050.0000.0010.1080.585
Payment_Behaviour_High_spent_Large_value_payments0.0530.2240.0950.0750.0000.0940.0860.0230.0540.0100.1120.1520.0950.0090.0400.4090.0280.0380.0340.0320.0900.0310.0240.0470.0310.0320.0000.0000.0000.0000.0070.0040.0000.0060.0000.0010.0000.0080.0000.0000.0000.0000.1020.1011.0000.1740.1390.1330.1600.2810.110
Payment_Behaviour_High_spent_Medium_value_payments0.0000.1790.0390.0220.0180.0310.0390.0040.0070.0000.0420.0560.0290.0180.0460.2670.0190.0150.0120.0000.0310.0080.0110.0120.0000.0190.0000.0000.0040.0080.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0310.0320.1741.0000.1580.1510.1830.3210.054
Payment_Behaviour_High_spent_Small_value_payments0.0000.0620.0130.0000.0000.0000.0000.0220.0160.0000.0000.0160.0190.0000.0350.0830.0070.0000.0000.0000.0090.0000.0110.0000.0000.0000.0040.0000.0040.0000.0040.0000.0070.0000.0050.0000.0090.0070.0080.0060.0070.0060.0130.0050.1390.1581.0000.1210.1460.2560.023
Payment_Behaviour_Low_spent_Large_value_payments0.0000.0380.0080.0000.0090.0000.0000.0110.0000.0110.0000.0290.0070.0080.0000.0890.0030.0000.0000.0000.0000.0000.0000.0070.0060.0000.0000.0000.0000.0050.0040.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.1330.1510.1211.0000.1390.2440.000
Payment_Behaviour_Low_spent_Medium_value_payments0.0120.1110.0000.0220.0180.0190.0070.0000.0000.0000.0000.0420.0000.0030.0600.1050.0130.0110.0100.0210.0040.0210.0000.0000.0160.0020.0000.0000.0030.0120.0000.0000.0000.0000.0030.0000.0020.0000.0040.0000.0040.0040.0000.0010.1600.1830.1460.1391.0000.2950.003
Payment_Behaviour_Low_spent_Small_value_payments0.0490.2670.1090.0840.0090.1030.0960.0000.0480.0000.1210.0920.1050.0000.0440.3490.0380.0500.0380.0360.0950.0310.0450.0480.0370.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.1080.2810.3210.2560.2440.2951.0000.135
Credit_Score0.2530.3120.5630.5500.0210.5450.5840.0010.2880.0000.6480.0970.5620.0130.1050.3250.2070.2070.2100.1990.5330.2120.1980.2170.1970.2130.0040.0000.0040.0000.0000.0050.0080.0000.0000.0110.0190.0160.0000.0000.0240.0030.5940.5850.1100.0540.0230.0000.0030.1351.000

Missing values

2023-06-05T20:58:19.119693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-05T20:58:19.716051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgeTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceAuto LoanCredit-Builder LoanDebt Consolidation LoanHome Equity LoanMortgage LoanNot SpecifiedPayday LoanPersonal LoanStudent LoanOccupation_AccountantOccupation_ArchitectOccupation_DeveloperOccupation_DoctorOccupation_EngineerOccupation_EntrepreneurOccupation_JournalistOccupation_LawyerOccupation_ManagerOccupation_MechanicOccupation_Media_ManagerOccupation_MusicianOccupation_ScientistOccupation_TeacherOccupation_WriterPayment_of_Min_Amount_NMPayment_of_Min_Amount_NoPayment_of_Min_Amount_YesPayment_Behaviour_High_spent_Large_value_paymentsPayment_Behaviour_High_spent_Medium_value_paymentsPayment_Behaviour_High_spent_Small_value_paymentsPayment_Behaviour_Low_spent_Large_value_paymentsPayment_Behaviour_Low_spent_Medium_value_paymentsPayment_Behaviour_Low_spent_Small_value_paymentsCredit_Score
0182930.131.05.0836.06.012.704.03846.4537.934665266.050.768440275.759795246.4845980100100100000000010000000100000100
1313080.566.06.024621.019.022.4513.032953.6837.895848115.0133.35590593.650442348.8139871300101000000000001000000010001000
2371910.701.04.01246.014.03.084.03479.8336.491037277.050.305036172.477693238.2874381000000210000000000010000100001001
3335163.1810.08.031824.018.012.499.013947.2421.74488458.0378.304673166.487676231.5254010100213010001000000000000010010000
4185998.104.06.032224.014.010.0014.022569.0927.350833114.091.35418866.232154692.2233251000010000000001000000000010100000
5253157.562.06.07219.014.07.683.031233.2438.323940343.039.548996155.936875410.2698790000001010010000000000000100000010
6294546.217.07.08516.019.015.344.022680.8738.70030581.0767.947345206.017263317.2679040100211001000000000000000010010001
7423080.564.05.019218.09.016.509.02335.0432.860511241.027.57867277.279445369.9108000010000100000000000001001000000010
8347702.345.06.05113.011.00.533.03118.8037.561138395.061.367912616.888909361.9769290000001000000000000100001000001000
9231747.666.010.029627.020.03.8710.012521.4029.262379116.054.72120174.865187335.1795290122000100000000100000000010000010
AgeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgeTotal_EMI_per_monthAmount_invested_monthlyMonthly_BalanceAuto LoanCredit-Builder LoanDebt Consolidation LoanHome Equity LoanMortgage LoanNot SpecifiedPayday LoanPersonal LoanStudent LoanOccupation_AccountantOccupation_ArchitectOccupation_DeveloperOccupation_DoctorOccupation_EngineerOccupation_EntrepreneurOccupation_JournalistOccupation_LawyerOccupation_ManagerOccupation_MechanicOccupation_Media_ManagerOccupation_MusicianOccupation_ScientistOccupation_TeacherOccupation_WriterPayment_of_Min_Amount_NMPayment_of_Min_Amount_NoPayment_of_Min_Amount_YesPayment_Behaviour_High_spent_Large_value_paymentsPayment_Behaviour_High_spent_Medium_value_paymentsPayment_Behaviour_High_spent_Small_value_paymentsPayment_Behaviour_Low_spent_Large_value_paymentsPayment_Behaviour_Low_spent_Medium_value_paymentsPayment_Behaviour_Low_spent_Small_value_paymentsCredit_Score
23403472488.923.06.02412.010.03.062.03220.3331.023495213.49092660.32284529.857576408.7118280000010120100000000000001000100000
23404221231.367.08.029338.019.08.886.012126.6726.396729192.00000037.104879102.261844273.7695271101000000000000000000100010000010
23405271800.809.05.025725.020.025.6711.013838.0033.404339165.00000063.36866349.648561327.0628591102002100000010000000000010010000
2340641764.338.04.017526.09.014.476.021225.0629.029478148.00000027.71767077.687855261.0276833000100010000000001000000010000010
23407141865.687.07.033534.015.021.559.032850.9622.700326213.49092665.797370134.355560258.8707593000000110000000010000000010000010
23408384896.567.05.07429.016.07.378.031419.9927.141606364.000000131.871482416.933866230.8503180001102000000000000100000100000011
23409371456.788.05.09515.019.011.306.02741.4632.41800066.00000045.804440142.100773237.7723700101110100000000000010000010000101
234103314836.740.05.04330.00.0-4.654.031104.3131.508604380.000000446.25934710000.000000309.4973620001100010000000000000100100001000
2341132468.778.06.029662.017.019.347.012924.7633.575214123.00000019.72792320.269324286.8797521001101200001000000000000010000100
23412411710.806.010.019554.020.028.0512.034323.5530.04584660.00000043.676463197.502352219.9016012120000000000000100000000010000010